Gaussian mixture modeling for indoor positioning WIFI systems

Alfakih, Marwan; Keche, Mokhtar; Benoudnine, H.

doi:10.1109/ceit.2015.7233072

Cited by 23 publications

(22 citation statements)

References 14 publications

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“…In [5,6], the multi-component problem has been noted. In [6], the authors illustrated that human behaviors in the measurement environment (absence, sitting or standing still, moving randomly, and moving specifically) result in the bi-modal phenomena in the experimental data.…”

Section: Faculty Of Electronics Hanoi University Of Industry 2 Natiomentioning

confidence: 99%

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Gaussian Mixture Modeling for Wi-Fi fingerprintingbased indoor positioning in the presence of censored data

Vu¹,

Le²

2019

VJSTE

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In complex indoor environments, due to the attenuation of the signal and the changing surrounding environment, the censoring and multi-component problems may be present in the observed data. Censoring refers to the fact that sensors on portable devices cannot measure Received Signal Strength Index (RSSI) values below a specific threshold, such as -100 dBm. The multi-component problem occurs when the measured data varies due to obstacles and user directions, whether the door is closed or open, etc. By accounting for these problems, this paper proposes to model the RSSI probability density distributions using the Censoring Gaussian Mixture Model (C-GMM) and develop the Expectation-Maximization (EM) algorithm to estimate the parameters of this model in the offline phase of the Wi-Fi fingerprinting based Indoor Positioning Systems (IPS). The simulation results demonstrate the effectiveness of the proposed method.

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Section: Faculty Of Electronics Hanoi University Of Industry 2 Natiomentioning

confidence: 99%

“…In this case, using a single Gaussian distribution to model the RSSI histogram is not appropriate. In [5], the Gaussian Mixture Model (GMM) was proposed to model the RSSI measurements. Positioning results were improved relative to the single Gaussian model.…”

Section: Faculty Of Electronics Hanoi University Of Industry 2 Natiomentioning

confidence: 99%

Gaussian Mixture Modeling for Wi-Fi fingerprintingbased indoor positioning in the presence of censored data

Vu¹,

Le²

2019

VJSTE

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“…Figure 4 illustrates the positioning process by WKNN (K = 6). The asterisk in this figure is the real position, whose coordinate is (9,4). In the localization process, six nearest neighbors (blue dots) are selected and the final location (green square) is (7.76, 3.90).…”

Section: Generating Candidate Locations By Wknnmentioning

confidence: 99%

“…Fras et al 8 combine weighted K-nearest neighbors (WKNN) with Bayes for a better localization accuracy. Alfakih et al 9 propose to determine the testing position by approximating the probability distribution of RSSI data using Gaussian mixture model (GMM). In deterministic algorithms, Rida et al 10 develop trilateration-based Bluetooth positioning procedure.…”

Section: Introductionmentioning

confidence: 99%

Bluetooth positioning based on weighted K-nearest neighbors and adaptive bandwidth mean shift

Wang

Sun

Zhang

et al. 2017

International Journal of Distributed Sensor Networks

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Bluetooth positioning is an important and challenging topic in indoor positioning. Although a lot of algorithms have been proposed for this problem, it is still not solved perfectly because of the instable signal strengths of Bluetooth. To improve the performance of Bluetooth positioning, this article proposes a coarse-to-fine positioning method based on weighted K-nearest neighbors and adaptive bandwidth mean shift. The method first employs weighted K-nearest neighbors to generate multi-candidate locations. Then, the testing position is obtained by applying adaptive bandwidth mean shift to the multi-candidate locations, which is used to search for the maximum density of the candidate locations. Experimental result indicates that the proposed method improves the performance of Bluetooth positioning.

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“…Ndeye et al [4] proposed a binary GMM to detect and exclude anomaly measurements in the location fingerprint database. Alfakih et al [5] used the GMM to approximate the probability distribution of received WIFI signal strength, thus locating the mobile position. However, the propagation path of the AP transmission signal is often blocked by objects, such as buildings and people moving in the practical application environment, resulting in the wave signal travels by refraction, reflection and other non-line-of-sight propagation.…”

Section: Introductionmentioning

confidence: 99%

A Method of WLAN Indoor Location Based on MMDM-Boost

Li¹,

Wen²

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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Abstract. Towards the problem of the time-varying AP signal strength degrades the indoor positioning accuracy in Wireless Local Area Network (WLAN), a WLAN indoor positioning method based on multiple mixed distribution model (MMDM) and Adaboost algorithm is adopted. Firstly, in order to describe the probability density distribution of AP signal strength accurately and improve the system suitability, the proposed method employs Gaussian mixture model, Binomial mixture model and Poisson mixture model to make up the MMDM and construct the fingerprint database. Secondly, in order to avoid the insufficient training of positioning model caused by lacking of training samples, the Adaboost is used to comprise the weak classifier based multiple MMDM into a strong classifier and improve the generalization ability of the system. Lastly, the mapping relation between fingerprint data and real position is also built by Adaboost classifier in online positioning stage. The experimental results show that the proposed method is superior to several indoor positioning algorithms with better time shifting adaptability and positioning accuracy.

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Gaussian mixture modeling for indoor positioning WIFI systems

Cited by 23 publications

References 14 publications

Gaussian Mixture Modeling for Wi-Fi fingerprintingbased indoor positioning in the presence of censored data

Gaussian Mixture Modeling for Wi-Fi fingerprintingbased indoor positioning in the presence of censored data

Bluetooth positioning based on weighted K-nearest neighbors and adaptive bandwidth mean shift

A Method of WLAN Indoor Location Based on MMDM-Boost

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